Training-Free Logical and Structural Anomaly Detection via Calibrated Fusion
This work shows that counting ability can be introduced into training-free anomaly detection without additional training or part-level supervision, and proposes a normal-set calibration that aligns heterogeneous anomaly cues using statistics from normal images, enabling their direct fusion within a unified training-fre...